Reproductive, maternal, newborn, and child health intervention coverage in 70 low-income and middle-income countries, 2000–30: trends, projections, and inequities

Summary Background Monitoring the progress in reproductive, maternal, newborn, and child health (RMNCH) using the composite coverage index (CCI) is crucial to evaluate the advancement of low-income and middle-income countries (LMICs) towards the attainment of Sustainable Development Goal target 3. We present current benchmarking for 70 LMICs, forecasting to 2030, and an analysis of inequities within and across countries. Methods In this cross-sectional secondary data analysis, we extracted 291 data points from the WHO Equity Monitor, and Demographic and Health Survey Statcompiler for 70 LMICs. We selected countries on the basis of whether they belonged to LMICs, had complete information about the predictors between 2000 and 2030, and had at least one data point related to CCI. CCI was calculated on the basis of eight types of RMNCH interventions in four domains, comprising family planning, antenatal care, immunisations, and management of childhood illnesses. This study examined CCI as the main outcome variable. Bayesian hierarchical models were used to estimate trends and projections of the CCI at regional and national levels, as well as the area of residence, educational level, and wealth quintile. Findings Despite progress, only 18 countries are projected to reach the 80% CCI target by 2030. Regionally, CCI is projected to increase in all regions of Asia (in southern Asia from 51·8% in 2000 to 89·2% in 2030; in southeastern Asia from 58·8% to 84·4%; in central Asia from 70·3% to 87·0%; in eastern Asia from 76·8% to 82·1%; and in western Asia from 56·5% to 72·1%), Africa (in sub-Saharan Africa from 46·3% in 2000 to 72·2% in 2030 and in northern Africa from 55·0% to 81·7%), and Latin America and the Caribbean (from 67·0% in 2000 to 83·4% in 2030). By contrast, southern Europe is predicted to experience a decline in CCI over the same period (70·1% in 2000 to 55·2% in 2030). Across LMICs, CCIs are higher in urban areas, in populations in which women have higher education levels, and in populations with a high income. Interpretation Governments of countries where the universal target of 80% CCI has not yet been reached must develop evidence-based policies aimed at enhancing RMNCH coverage. Additionally, they should focus on reducing the extent of existing inequalities within their populations to drive progress in RMNCH. Funding Hitotsubashi University and Japan Society for the Promotion of Science.

Appendix e-method 1: Coverage Composite Index (CCI) computation CCI is the weighted average of percentage coverage of the eight interventions along four stages of continuum of care: reproductive care; maternal care; childhood immunization; and management of childhood illness.For the current study, data are derived from re-analysis of Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS) micro-data using the standard indicator definitions as published in DHS and MICS.
Information was obtained and on women aged 15-49 years and children aged less than 5 years.The index is calculated as below: where DFPSm is demand for family planning satisfied with modern methods; ANC4 is 4+ antenatal care visits with any provider; SBA is skilled birth attendant; BCG is bacille Calmette-Guérin vaccine, DPT3 is three doses of Diphtheria, Pertussis, and Tetanus vaccine; MSL is measles immunization; ORS is oral rehydration salts; and CAREP is care seeking for suspected pneumonia.The indicators had equal weights, except for DPT3, which received a weight of two because it requires more than one dose or more than one contact with the provider.
1) DFPSm: It is defined as the percentage of women of reproductive age (15-49 years) whose demand for family planning is satisfied with modern methods.These include oral contraceptive pill, condoms (male and female), intrauterine devices, sterilization (male and female), injectables, implants (e.g., Norplant), diaphragm, spermicidal agents (foam or jelly), patches, vaginal ring, lactational amenorrhea method (LAM) and emergency contraception (the day-after pill).The numerator is the percentage of women of reproductive age (15-49 years old) who are currently using, or whose partner is currently using, at least one modern contraceptive method.The denominator is the total demand for family planning [the sum of contraceptive prevalence (any method) and the unmet need for family planning].
2) SBA: It is defined as the proportion of births attended by skilled health personnel (generally doctors, nurses or midwives but can refer to other health professionals providing childbirth care).It is calculated as the number of births attended by skilled health personnel (doctor, nurse and/or midwife) expressed as total number of live births in the same period [(Number of births attended by skilled health personnel / Total number of live births) x 100].
In the DHS and MICS surveys, the respondent is asked about each live birth and who had helped them during childbirth for a period up to five years (or three years) before the interview.
3) ANC4: It is defined as the percentage of women aged 15-49 with a live birth in a given time period that received antenatal care four or more times.It is calculated as (Number of women aged 15-49 attended at least four times during pregnancy by any provider for reasons related to the pregnancy / Total number of women aged 15-49 with a live birth in the same period) x 100.The indicator is based on a standard question that asks if and how many times the health of the woman was checked during pregnancy, and due to data limitations, it is not possible to determine the type of provider for each visit.

4) DPT3:
It is defined as the percentage of one-year-olds (aged between 12-23 months) who received three doses of the combined diphtheria, pertussis and tetanus toxoid vaccine in a given year.The indicator is derived by dividing the total number of vaccinations given by the number of children in the target population and the vaccine time is estimated before the DHS and MICS surveys.

Socio-demographic index (SDI):
A socio-demographic index (SDI) is a measure of socioeconomic development that can be used to compare countries at a global level.It is an important indicator of a country's health outcomes 1 and is calculated by taking into account a variety of factors, such as per capita income, total fertility rate, and educational attainment. 2 SDI is calculated using lag-distributed per capita income, total fertility rate, and educational attainment data.Countries with a SDI value between 0 and 1 typically display lower educational attainment, lower per capita income, and higher total fertility rates than those near 1.Conversely, countries with a SDI value close to 1 tend to have higher incomes and educational attainment, as well as lower fertility rates.The Global Burden of Disease Study has provided SDI values for countries from 2000-2030, providing a useful snapshot of socioeconomic development during this period. 2The SDI data can be found at http://www.healthdata.org/health-financing/.
Per capita total health expenditure: Data on per capita total health expenditures in constant 2018 US dollars for the period 2000-2016 and forecasted per capita total health expenditures for the period 2017-2030 were obtained from studies conducted by the Institute for Health Metrics and Evaluation (IHME), which gathered data from studies conducted by the Institute.IHME forecasted total health expenditures per capita using health expenditure estimates for 1995-2016, taking into account factors such as total fertility rate, proportion of elderly population (65 years and older), per capita GDP, and total government spending.Health expenditures for 2000-2016 have been compared with projected values for 2017-2030 to determine total health expenditures per capita.GDP spent on health data can be found at http://www.healthdata.org/health-financing/.

GDP per capita:
Gross domestic product (GDP) per capita is an important economic measure used by the International Monetary Fund (IMF) to compare the economic output of countries worldwide.GDP per capita is a measure of the total economic output of a country divided by its population.GDP per capita data was obtained from the IMF for the years 2000-2027, and then for 2028 to 2030 we assumed the same GDP per capita as in 2027.This GDP data comes from the IMF's website: https://www.imf.org/en/Publications/WEO/.

Wealth index
As a proxy measure of socioeconomic status, the wealth index is widely used in low and middle income countries as an asset-based measure of household wealth. 3Wealth index information is usually included in nationally representative surveys such as the DHS and MICS.An index of household wealth is typically calculated based on several factors, such as the availability of water and sanitation facilities.An estimation of a wealth score is then performed using principal component analysis (PCA), which represents a household's position relative to the rest of the households in a country. 4,5To determine a relative measure of household wealth, all households in a survey are divided into quintiles, and the lowest 20% values represent the poorest 20% of households (i.e., the poorest 20% of households), and the highest 20% values represent the wealthiest households.

Appendix e-method 3: Bayesian model
Bayesian approach was used and favoured with the aim to project probabilities, which would not be possible using a frequentist approach.The essential difference between the two approaches is how probability is used.If we were to use a frequentist approach, the 95% CIs cannot calculate the probability of observing a future value, and hence we would be limited to using probability to only model certain processes (using process of "sampling").For the purpose of our study, we were required to use probability more widely to model both sampling and other kinds of uncertainty which could only be conducted using Bayesian approach.As a result of the advantage to produce probabilistic-oriented inferences, Bayesian methods are increasingly being applied to scientific fields, particularly in ecology where many cases outperform deterministic approaches.Since ecological modelling is characterized by high uncertainty due to the complex and often unknown cause-effect relationships among variables, a probabilistic approach is necessary to yield distributions of possible outcomes -in essence, transforming uncertainty into probability thresholds.
This unique advantage of Bayesian approach over frequentist approach was key to conducting our study.Another advantage of using Bayesian methods in our study was the ability to combine prior knowledge about parameters with evidence from data.This method is favored for analysis of hierarchical models such as our study, which enables: flexibility in specifying hierarchical structures of parameters using priors; ability to handle small samples and model misspecification (overparameterization of the likelihood can be resolved with well-chosen priors); explicit handling of uncertainty; and intuitive and easy interpretation of results (credible interval versus confidence interval).In frequentist approach, confidence interval (CI) talks about central area that contains 95% of distribution.Clearly, 95% CI means that with a large number of repeated samples, 95% of such calculated CIs would include the true value of the parameter.In the Bayesian approach, credible interval (CrI) provides an interval in which there is a 95% change of parameter lying, we can drive this directly from our posterior distribution.The 95% CI included the fixed parameter 95% of the trials under the null model, whereas the 95% CrI contains the parameter with a probability of 0.95.
The following Bayesian hierarchical model was used to estimate the trends in, and projections of CCI up to 2030, at country and residence level: The model incorporated covariates that aid in predicting CCI, including year, residence area, and time-varying country-level factors such as SDI, GGDPH, and GDPC.Note that   = {  ,   ,   } represents national, urban, and rural levels and is encoded as a dummy variable.After estimating the regression parameters, we can predict the CCI at the national, urban, and rural levels.The term   gives the random intercept.The error term   quantifies random variations in CCI that are not explained by the covariates.We also incorporated interactions of residence with country to account for potential changes in associations over time, particularly in relation to the availability of treatments for maternal and child health.

Table S1 :
PubMed search January 25, 2023 It is defined as the percentage of children under 5 years of age with symptoms of pneumonia [cough and difficult breathing (not due to a problem in the chest and a blocked nose)] in the two weeks preceding the survey taken to an appropriate health facility or provider.The definition is based on the mother's perceptions of a child who has a cough; is breathing faster than usual with short, quick breaths; or is having difficulty breathing, excluding children who had only a blocked nose.Mothers or caregivers of children under five years are asked if the child had symptoms of acute respiratory infection (ARI), and if so, whether treatment was sought and where it was sought.
1,   +  2,   +  3,   +  4,   +  5,   +  where   is the logit-transformed probability corresponding to the CCI in the ith year for the jth country in the kth region, and   quantifies random variations in CCI that were not explained by covariates.The random intercept   was modeled hierarchically by country j and region k:

Table .
SDI, SDI, sociodemographic index, DAH, development assistance for health; HRH, human resource for health; GGDPH, government gross domestic product spending on health; and GDPC, and GDP per capita;

Table S6 :
National level estimate of Gelman Rubin Potential scale reduction factors (PSRF)